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• W2510952020 abstract "In this paper, we propose a data-driven fault detection algorithm for the nonlinear system with network-induced time delay. Double-layer Quasi Takagi-Sugeno fuzzy modeling method is employed to represent the system. Quasi fuzzy model addresses the issue of time delay on the inner layer. Classical fuzzy model depicts the nonlinearity of the networked control system (NCS) on the outer layer. We apply implicit model approach to achieve the data-driven fault detection for the system. The implicit model approach provides an alternative perspective of the recently developed subspace-based data-driven approach. Inspired by the method of parallel distributed compensation, we construct the overall data-driven fault detection algorithm for the nonlinear NCS. The validation of the proposed algorithm is proved on the well-established Tennessee-Eastman process." @default.
• W2510952020 created "2016-09-16" @default.
• W2510952020 creator A5022113101 @default.
• W2510952020 creator A5034069559 @default.
• W2510952020 date "2016-07-01" @default.
• W2510952020 modified "2023-09-26" @default.
• W2510952020 title "Data-driven fault detection for networked control system based on implicit model approach" @default.
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• W2510952020 doi "https://doi.org/10.1109/chicc.2016.7554806" @default.
• W2510952020 hasPublicationYear "2016" @default.
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